Chi-Wing Fu
Computer Vision and Pattern Recognition · 2007–2021
A researcher in the Science corpus: 20 papers; trusts 1,230 researchers and is trusted by 23 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 425 citations into it; #8,025 of 21,575 by trust here, #3,404 by citations
- Computational Mechanics — 148 citations into it; #3,317 of 10,001 by trust here, #1,556 by citations
- Artificial Intelligence — 146 citations into it; #20,536 of 35,345 by trust here, #6,517 by citations
- Aerospace Engineering — 95 citations into it
- Geology — 74 citations into it; #325 of 687 by trust here, #173 by citations
- Radiology, Nuclear Medicine and Imaging — 64 citations into it
- Computer Graphics and Computer-Aided Design — 60 citations into it; #599 of 1,584 by trust here, #605 by citations
- Model Architectures — 56 citations into it
Whom Chi-Wing Fu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Diederik P. Kingma
- Geoffrey E. Hinton
- Alex Krizhevsky
- Ross B. Girshick
- Yoshua Bengio
- Yann LeCun
- Karen Simonyan
- Charles Ruizhongtai Qi
- Ilya Sutskever
Who trusts Chi-Wing Fu overall
- He Zhang
- Tianwei Yin
- Ruihui Li
- Yifan Wang
- Xiangde Luo
- Daniel Cohen-Or
- Xu Yan
- Xiaodong Cun
- Wenhan Yang
- Xueyang Fu
Papers from Chi-Wing Fu's perspective
Ordered by how strongly the researchers Chi-Wing Fu trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- The Liver Tumor Segmentation Benchmark (LiTS) (2019) — 1,500 citations
- Underexposed Photo Enhancement Using Deep Illumination Estimation (2019) — 957 citations
- H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes (2018) — 2,217 citations
- Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization (2020) — 231 citations
- SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud (2021) — 401 citations
- Solid texture synthesis from 2D exemplars (2007) — 308 citations
- CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud (2020) — 340 citations
- Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation (2019) — 495 citations
- Joint Bi-layer Optimization for Single-Image Rain Streak Removal (2017) — 333 citations
- 3D multi‐scale FCN with random modality voxel dropout learning for Intervertebral Disc Localization and Segmentation from Multi‐modality MR Images (2018) — 138 citations
- PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation (2020) — 584 citations
- PU-GAN: A Point Cloud Upsampling Adversarial Network (2019) — 514 citations
- Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation (2019) — 1,259 citations
- PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing (2019) — 821 citations
- Difficulty-aware Meta-Learning for Rare Disease Diagnosis (2019) — 75 citations
- Depth-Attentional Features for Single-Image Rain Removal (2019) — 400 citations
- CANet: Cross-Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading (2019) — 367 citations
- EC-Net: an Edge-aware Point set Consolidation Network (2018) — 296 citations
- SV-RCNet: Workflow Recognition From Surgical Videos Using Recurrent Convolutional Network (2018) — 318 citations
- PU-Net: Point Cloud Upsampling Network (2018) — 730 citations